1. Field of the Invention
The invention relates generally to a method and system for the computerized analysis of radiographic images, and more specifically, to the determination of the likelihood of malignancy in pulmonary nodules using artificial neural networks (ANNs).
The present invention generally relates to computerized techniques for automated analysis of digital images, for example, as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; 5,740,268; 5,790,690; and 5,832,103; as well as U.S. patent applications Ser. No. 08/158,388 (PCT Publication WO 95/14431); Ser. Nos. 08/173,935; 08/220,917 (PCT Publication WO 95/26682); Ser. No. 08/398,307 (PCT Publication WO 96/27846); Ser. No. 08/523,210 (PCT Publication WO 95/15537); Ser. Nos. 08/536,149; 08/562,087; 08/757,611; 08/758,438; 08/900,191; 08/900,361; 08/900,362; 08/900,188; 08/900,189, 08/900,192; 08/979,623; 08/979,639; 08/982,282; 09/027,468; 09/027,685; 09/028,518; 09/053,798; 09/092,004; 09/098,504; 09/121,719; 09/131,162; 09/141,535; 09/156,413; No. 60/107,095 (Attorney Docket No. 0730-0060-20PROV, filed Nov. 5, 1998) all of which are incorporated herein by reference.
The present invention includes the use of various technologies referenced and described in the above-noted U.S. Patents and Applications, as well as described in the references identified in the appended APPENDIX and cross-referenced throughout the specification by reference to the corresponding number, in brackets, of the respective references listed in the APPENDIX, the entire contents of which, including the related patents and applications listed above and the references listed in the APPENDIX, are incorporated herein by reference.
2. Discussion of the Background
Although a solitary pulmonary nodule (SPN) is a common finding on a chest radiograph, the differential diagnosis of a solitary pulmonary nodule or chest radiograph is often a difficult task for radiologists [1-8]. Since a solitary pulmonary nodule may be the first sign of lung cancer, especially in its early stage, most patients undergo a further diagnostic evaluation that may include an imaging study with computed tomography (CT) [1]. Malignant diseases are estimated to occur in about 20% of patients with solitary pulmonary nodules in the population [9]. The majority of radiographically detected pulmonary nodules, however, are benign [3,8,10-14].
Although CT has become a major diagnostic method to differentiate pulmonary nodules in recent years, a large number of CT examinations have been performed for benign cases that were suspected of being malignant. A survey was conducted to obtain estimates for the relative numbers (percentages) of malignant and benign cases that were performed for chest CT study under the investigation of a solitary pulmonary nodule. The survey was performed at the University of Chicago Hospital and at four Hospitals in Japan (University of Occupational and Environmental Health Hospital, Fukuoka; Nagasaki University Hospital, Nagasaki; Iwate Prefectural Central Hospital, Morioka; and Tokyo Metropolitan Hospital, Tokyo). At each institution, patients who underwent chest CT examinations for suspicious pulmonary nodules on chest radiographs were assessed regarding pre-CT clinical diagnosis, the final diagnosis, patient""s age, and patient""s gender. Final diagnosis was established by a pathologic examination or clinical follow-up.
One hundred thirty-three patients (83 male, 50 female) who ranged in age from 25 to 85 (mean, 62.9 years) were identified at the five hospitals. Pre-CT clinical diagnosis consisted of xe2x80x9csuspicion of lung cancerxe2x80x9d for 43 of the patients, xe2x80x9clung nodule/lung massxe2x80x9d for 70 of the patients, xe2x80x9cabnormal shadowxe2x80x9d for 10 of the patients, xe2x80x9csuspicion of pulmonary metastasisxe2x80x9d for 6 of the patients, and benign diseases for four of the patients (xe2x80x9csuspicion of pulmonary tuberculosisxe2x80x9d for one patient, xe2x80x9csuspicion of pulmonary abscessxe2x80x9d for two patients, and xe2x80x9csuspicion of pulmonary aspergillosisxe2x80x9d for one patient). In the cases in which pre-CT diagnosis included a lung nodule/lung mass, some of the cases may have involved suspected benign diseases; however, it was assumed that most of these cases involved suspected malignancy.
Table 1 shows the summary of the survey on the final diagnosis of solitary pulmonary nodules which underwent chest CT. Fifty-five out of 133 cases (or 41.4%) indicated malignant nodules including primary lung cancer and pulmonary metastases. Sixty-four cases (48.1%) indicated benign conditions including benign diseases and xe2x80x9cnegativexe2x80x9d cases that had no apparent lung abnormality as a result of CT examination. Fourteen cases had inconclusive final diagnoses. The results obtained in this survey show that a large fraction of patients who underwent chest CT examination were ultimately identified as having benign conditions. Accordingly, some of the CT examinations may have been avoided if these benign conditions were diagnosed accurately and/or confidently on the initial chest radiographs.
Computer schemes capable of providing objective information on the nature of pulmonary nodules may aid radiologists in their classification of pulmonary nodules. Various computerized schemes have been investigated for characterizing pulmonary nodules.
In most of these studies, however, radiographic features were manually extracted, and the computer was used only to determine the likelihood of malignancy by merging image features using rule-based or discriminant analysis. Swensen et al. [15] estimated the probability of malignancy in radiologically indeterminate SPNs by using multivariate logistic regression. They concluded that three clinical parameters (age, cigarette-smoking status, and history of cancer) and 3 radiological features (diameter, spiculation, and upper lobe location) were independent predictors of malignancy. Cummings et al. [16] estimated the probability of malignancy of pulmonary nodules by using Bayesian analysis based on the diameter of an SPN, the patient""s age, history of cigarette smoking, and the prevalence of malignancy in SPNs. Gurney [17,18] also used Bayesian analysis to calculate the probability of malignancy, which was compared with the performance of radiologists.
Other investigators have used computer-extracted features to differentiate between malignant and benign lung nodules. Sherrier et al. [19] applied a gradient analysis for distinguishing benign nodules from malignant nodules, and they presented that benign calcified granuloma showed greater gradient number than malignant nodules. Sasaoka et al. [20] extracted nodule features using a computerized method. However, the extracted features, such as density gradient and density entropy, were not directly correlated with specific radiological findings, and thus the meaning of these features is inconclusive. Recently, artificial neural networks (ANNs) have been used in the field of diagnostic radiology to provide a potentially powerful classification tool [12-27]. Gurney et al. [28] reported that the Bayesian method was better than the neural network in the prediction of the probability of malignancy in pulmonary nodules. Despite these considerable efforts, a computerized scheme has not been applied in clinical situations to assist radiologists in their interpretation of malignancy of pulmonary nodules.
Accordingly, an object of this invention is to provide a new and improved method and system for the analysis of the likelihood of malignancy in solitary pulmonary nodules using artificial neural networks.
Another object of the present invention is to provide a method and system for implementing a computer-aided diagnostic (CAD) technique to assist radiologists in distinguishing benign and malignant lung nodules.
Another object of this invention is to provide a method and system for assisting radiologists in accurately identifying benign pulmonary nodules.
These and other objects are achieved according to the invention by providing (1) a new and improved method, (2) a storage medium storing a program for performing the steps of the method, and (3) a system for analyzing nodules. The method, on which a computer program product and system is based, includes obtaining a digital outline of a nodule; generating objective measures corresponding to physical features of the outline of the nodule; applying the generated objective measures to an artificial neural network (ANN); and determining a likelihood of malignancy of the nodule based on an output of the ANN.
Techniques include the use of ANNs to merge subjective features extracted by radiologists to determine the likelihood of malignancy of solitary pulmonary nodules. Additional techniques include computerized extraction of objective measures of lung nodules that are correlated to the subjective features seen by radiologists, and the use of ANNs to estimate the likelihood of malignancy by merging the objective measures. The performance of the ANNs is evaluated by means of receiver operating characteristic (ROC) analysis. The performance of radiologists is evaluated in classifying benign and malignant nodules for comparison with the computerized methods.
The present invention thus addresses the problems associated with the conventional diagnosis of pulmonary nodules. The method and system of the invention, using ANNs to merge subjective data obtained manually or objective data obtained with automated techniques, is thus able to estimate the likelihood of malignancy. This estimate assists radiologists in confidently and accurately identifying benign nodules, thereby helping to reduce the number of unnecessary CT examinations (i.e., CT examinations performed on patients with benign nodules).